Graphical models, including constraint networks, Bayesian networks, Markov
random fields and influence diagrams, have become a central paradigm for
knowledge representation and reasoning in Artificial Intelligence, and
provide powerful tools for solving problems in a variety of application
domains, including coding and information theory, signal and image
processing, data mining, learning, computational biology, and computer
vision. Although past decades have seen considerable progress in algorithms
in graphical models, many real-world problems are of such size and
complexity that they remain out of reach. Advances in exact and approximate
inference methods are thus crucial to address these important problems with
potential impact across many computational disciplines. Exact inference is
typically NP-hard, motivating the development of approximate and anytime
techniques.
After summarizing the main principles behind the AND/OR search guided by
heuristics based on variational inference (e.g., weighted mini-bucket and
cost-shifting schemes) for graphical model queries, I will focus on recent
work for solving the marginal map task, a query that combines, and
generalizes, optimization and summations queries and is far harder than
both. The emerging solvers aim for anytime behavior that generate not only
an approximation that improves with time, but also upper and lower bounds
which become tighter with more time.
Short Bio.
==========
Rina Dechter's research centers on computational aspects of automated
reasoning and knowledge representation including search, constraint
processing, and probabilistic reasoning. She is a Chancellor's Professor of
Computer Science at the University of California, Irvine. She holds a Ph.D.
from UCLA, an M.S. degree in applied mathematics from the Weizmann
Institute, and a B.S. in mathematics and statistics from the Hebrew
University in Jerusalem. She is an author of Constraint Processing published
by Morgan Kaufmann (2003), and Reasoning with Probabilistic and
Deterministic Graphical Models: Exact Algorithms by Morgan and Claypool
publishers, 2013, has co-authored close to 200 research papers, and has
served on the editorial boards of: Artificial Intelligence, the Constraint
Journal, Journal of Artificial Intelligence Research (JAIR), and Journal of
Machine Learning Research (JMLR). She is a Fellow of the American
Association of Artificial Intelligence 1994, was a Radcliffe Fellow
2005-2006, received the 2007 Association of Constraint Programming (ACP)
Research Excellence Award, and she is a 2013 ACM Fellow. She has been
Co-Editor- in-Chief of Artificial Intelligence since 2011. She is also
co-editor with Hector Geffner and Joe Halpern of the book Heuristics,
Probability and Causality: A Tribute to Judea Pearl, College Publications,
2010.
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Refreshments will be served from 11:15
Lecture starts at 11:30